The present invention relates to an inference system suited to general expert systems in which problems are solved by utilizing the experience and knowledge items of experts which are set in a computer.
This invention also relates to a reasoning method suitable for expert systems in general for executing a judgement based on logical knowledge and intuitive knowledge.
For an expert system which assists the action and will of a human being, or is substituted therefor, there is a method utilizing knowledge in which the know-how of experts is expressed by inference rules, of the "If-then" type, and by frames storing knowledge items on the states and structures of an object to-be-handled. In this method, a forward/backward inference (hereinbelow termed "frame inference") is as follows.
A forward inference procedure typically includes:
(1) the step of comparing the If-part proposition of the inference rule with the existing state or the state of the object described in the frame, and PA1 (2) the step of executing the Then-part proposition of the inference rule selected on the basis of certainty intuitiveness or the like. PA1 (1) the step of selecting the rule in which the Then-part proposition is equal to a goal; PA1 (2) the step of checking if all If-part propositions of the selected rule satisfy a state or the state of the object described in the frame; PA1 (3) the step of regarding the goal as a conclusion when the all If-part propositions satisfy step (2), and proceed to step (6); PA1 (4) the step of going to step (1) when there is an If-part proposition which does not satisfy step (2); PA1 (5) the step of regarding the If-part proposition of the selected rule as a new goal when truth of the proposition cannot be judged, and going to step (1); and PA1 (6) the step of executing the goal.
A backward inference procedure typically includes:
This inference is strong in the treatment of logical thinking, such as a syllogism based on certain knowledge, but it has problems given that the know-how must be precisely described, and that expression of uncertain or intuitive knowledge is difficult.
In addition to the foregoing, systems employing "fuzzy" reasoning, which systems apply the fuzzy reasoning theory, are becoming increasingly popular as an expedient for expressing the inexactness or fuzziness of human knowledge.
Such fuzzy reasoning estimates a current state fuzzily and arrives at a problem solution by use of inference rules which are defined in the form of membership functions. With fuzzy reasoning, the treatment of fuzzy knowledge is permitted, and know-how may be described roughly.
Fuzzy reasoning, however, has a drawback given that propagation error of fuzziness progressively increases in a multistage inference operation.
In general, a human being determines his will for a given situation through a combination of uncertain knowledge and certain knowledge. Both the uncertain knowledge and the certain knowledge must be dealt with appropriately in order that inferences may be drawn by large-scale use of knowledge items in conjunction with a decision making process.
There has recently been proposed an expert system in which performance is enhanced by using a hybridization of the two inferencing systems such that each one compensates for the drawbacks of another one Such a system has been proposed in "Fuzzy Operation Function of ESHELL/SB" (Proceedings of the First Conference of Japan Society for Artificial Intelligence, in 1987, pp. 25-28).
The proposed expert system operates on a work station in an operation in which a membership function is defined in a frame. Suitability to a state is calculated from the membership function, and is used for derivation of an inference as a certainty. That is, a method in which the fuzzy reasoning incorporated into the frame inference scheme is adopted.
The afore-noted hybridized inferencing technique does not take it into account that human beings judge situations by utilizing both uncertain knowledge and certain knowledge. This has resulted in a problem that in inference schemes, it is inevitable that expressions of rules descriptive of the human knowledge items be inefficiently redundant. As a result, a long time must be expended for putting the human knowledge items into rules.
Moreover, in cases in which the earlier hybridized system is applied to an object system for which real time processing is required, the redundancy problem renders the scale of the system itself large and the response thereof slow. As a result of this problem, the earlier system is not used as an application per se, but is used as preestimative system for the application. Accordingly, the knowledge items, such as rules, constructed in the preestimative system in a work-station environment cannot be directly mounted on a microcomputer for to realizing real time processing. This has led to a further problem that a transplanting of the knowledge items into a microcomputer environment requires a great amount of modifications and time investments.
Further, man sometimes decides the conclusion after he expects the result This intuitive thought is executed by the predictive fuzzy reasoning method which was proposed in "A Predictive Fuzzy Control for Automatic Train Operation" (System & Control, vol. 28, No. 6, pp 605-613, 1984). This method uses rules derived from skilled human experiences and selects the most likely conclusion based on the prediction of results and on the direct evaluation to achieve an object efficiently and effectively.
However, this method is not combined with a frame engine, and higher efficiency is not attained.
Hereinafter, this method will be explained in more detail. Expert systems for executing for intuitive judgement and logical judgement of man have been examined in various fields. As the assistant tool, systems for executing for intuitive judgement by fuzzy reasoning and logical judgement by frame inference are disclosed in "Fuzzy Operation Function of ESHELL/SB" (Proceedings of the First Conference of Japan Society for Artificial Intelligence, 1987, pp. 25-28) . The fuzzy reasoning discussed in the prior art reference dealt with hereby can be said to be a state evaluation type fuzzy reasoning which evaluates a state value as it is.
On the other hand, the above predictive fuzzy reasoning system makes predictive calculation from a state value by use of a procedure and evaluates the predicted value (water temperature after five minutes, for example).
This system describes the human knowledge by use of a predictive fuzzy rule with the form of:
When (heating power is set to 1)
If (water temperature after five minutes is just good)
Then (heating power is set to 1).
A reasoning engine assumes When-part in each rule of the above type for one rule group, calculates the state of the If-part (water temperature after five minutes in the case described above) by use of a procedure and determines the evaluation value of each rule. Here, the minimum evaluation value is adopted when a plurality of conditions of the If-part exist. Next, the Then-part of the rule having the highest evaluation value is adopted as the conclusion. The above is the summary of the processing procedures of the predictive fuzzy reasoning.
The expert system described above does not take into account the point that the intuitive judgement is derived after the conclusion is predicted, and involves the problem that expert system configuration by the predictive fuzzy reasoning is difficult.